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And like any toolbox, the contents are tools – not processes, procedures, or algorithms. Machine Learning provides these components.

Supervised learning algorithms are trained on labelled examples, i.e., input where the desired output is known. The supervised learning algorithm attempts to generalise a function or mapping from inputs to outputs which can then be used speculatively to generate an output for previously unseen inputs.

Unsupervised learning algorithms operate on unlabelled examples, i.e., input where the desired output is unknown. Here the objective is to discover structure in the data (e.g. through a cluster analysis), not to generalise a mapping from inputs to outputs.

Note: many possible boundaries between black and white dots

plot_iris.py

DEMO

i.e. many logistic models can work the same on training data, some are better than others. We can’t tell.

11.
That sounds like Artificial Intelligence
Machine Learning is a branch of
Artificial Intelligence

12.
That sounds like Artificial Intelligence
ML focuses on systems that learn from
data
Many AI systems are simply programmed
to do one task really well, such as playing
Checkers. This is a solved problem, no
learning required.

30.
Feature Space Manipulation
Feature spaces are important!
Many machine learning tasks are solved by
selecting the appropriate features to define a
useful feature space

31.
Task: Classification
Classification is the act of placing a new data point
within a defined category
Supervised learning task
Ex. 1: Predicting customer gender through shopping
data
Ex. 2: From features, classifying an image as a car or
truck

34.
Linear Classification
Another way to think
of this is that we
want to draw a line
(or hyperplane) that
separates datapoints
from different
classes

35.
Sometimes this is easy
Classes are well
separated in this
feature space
Both H1 and H2
accurately separate
the classes.

36.
Other times, less so
This decision boundary works for most data points,
but we can see some incorrect classifications

37.
Example: Iris Data
There’s a famous dataset published by R.A.
Fisher in 1936 containing measurements of
three types of Iris plants
You can download it yourself here:
http://archive.ics.uci.edu/ml/datasets/Iris

55.
Learning
In all cases so far, “learning” is just a matter of
finding the best values for your weights
Simply, find the function that fits the training
data the best
More dimensions more features we can
consider

56.
What are we doing?
Logistic regression is actually maximizing the
likelihood of the training data
This is an indirect method, but often has good
results
What we really want is to maximize the accuracy
of our model

57.
Support Vector Machines (SVMs)
Remember how a large number of lines could
separate my classes?

58.
Support Vector Machines (SVMs)
SVMs try to find the optimal classification
boundary by maximizing the margin between
classes